
import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


#https://geojson.xyz/
# Define the list of coastal states
w_coast = ['Arizona', 'California', 'Idaho',
           'Montana', 'Nevada', 'New Mexico',
           'Oregon', 'Utah', 'Washington', 'Wyoming']

# Load the GeoJSON file
url = 'https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_admin_1_states_provinces_shp.geojson'
df = gpd.read_file(url)

# Filter the DataFrame to include only coastal states and select columns
df = df[df.name.isin(w_coast)][['name','geometry']]   #select columns

# Reproject geometries to a projected CRS (e.g., EPSG:3857)
df = df.to_crs('EPSG:3857')

# Calculate centroids
df['centroid'] = df.centroid  

# Extract longitude and latitude
df['lon'] = [i.xy[0][0] for i in df.centroid]        
df['lat'] = [i.xy[1][0] for i in df.centroid]         
df.head()


# Import the contextily library
import contextily
plt.rcParams.update({'font.size': 15})

fig, ax = plt.subplots(figsize=(18, 9))

#plotting geometry data
df.plot(column ='name', alpha=0.9, legend=False,      
        edgecolor = 'white', cmap='viridis', ax=ax)
df.apply(lambda x: ax.annotate(text = x['name'],      #annotations
                               xy = x.geometry.centroid.coords[0],
                               color = 'white',
                               ha = 'center'), axis = 1)

#Using OpenStreetMap.Mapnik to visualize the map
ax = contextily.add_basemap(ax, crs=df.crs.to_string(), source=contextily.providers.OpenStreetMap.Mapnik)
plt.show()


#Read the html file from wikipedia
df_w= pd.read_html('https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_income', 
                    match='States and territories ranked by median household income') 
df_w= df_w[0].rename(columns={'States and Washington, D.C.':'name'})
df_w= df_w.iloc[:,1:-1]
df_w.head()


# Do a Merge Operation of df and df_w
df_m = pd.merge(df, df_w, on='name')
df_m.head()


#Remove the year 2020
y_list = [str(i) for i in list(range(2010,2022))]
y_list.remove('2020')

# Convert Household Income from string to Integers
for i in y_list:
    df_m[str(i)+'_v'] = df_m[i].apply(lambda x: int(str(x).replace('$','').replace(',','')))
df_m.head()


# Plot choropleth maps for each year and save
v_list = [i+'_v' for i in y_list]
for year,val in zip(y_list, v_list):
    ax = df_m.plot(column = val,                          #plotting choropleth maps
                   edgecolor = 'white',
                   vmin = 40000, vmax = 85000, alpha=0.9,
                   figsize = (9, 6.5), cmap='viridis',
                   legend=False)
    
    df_m.apply(lambda x: ax.annotate(text = x['name'] +'\n'+ x[year],    #annotaion
                                     color = 'white',
                                     xy = x.geometry.centroid.coords[0],
                                     ha = 'center'), axis = 1)
    ax = contextily.add_basemap(ax, source=contextily.providers.OpenStreetMap.Mapnik, zoom=5) 
    plt.legend(title=year, frameon=False,
               bbox_to_anchor=(0.05, 0.05), loc='lower left')
    plt.tight_layout()
    plt.savefig(r'C:\Users\Owner\Desktop\GA_visualization_proj\choropleth_'+year+'.png')

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.


import matplotlib.pyplot as plt
import contextily

def get_collage(cols_n, rows_n, width, height, y_list, save_name):
    fig, axs = plt.subplots(rows_n, cols_n, figsize=(width/100, height/100))
    
    for i, year in enumerate(y_list):
        ax = axs[i // cols_n, i % cols_n]
        ax.set_title(year)
        df_m.plot(column=year+'_v', ax=ax, edgecolor='white', vmin=40000, vmax=85000, alpha=0.9, cmap='viridis', legend=False)
        df_m.apply(lambda x: ax.annotate(text=x['name'] + '\n' + str(x[year]), color='white', xy=x.geometry.centroid.coords[0], ha='center'), axis=1)
        contextily.add_basemap(ax, source=contextily.providers.OpenStreetMap.Mapnik, zoom=5) 
    
    plt.tight_layout()
    plt.savefig(save_name)

# output width = number of columns * figure width
# output height = number of rows * figure height
get_collage(3, 4, 900 * 3, 650 * 4, y_list, r'C:\Users\Owner\Desktop\GA_visualization_proj\choropleth_collage.png')


import matplotlib.pyplot as plt
import contextily
from matplotlib.animation import FuncAnimation

# Function to update the plot for each frame of the animation
def update_map(frame):
    ax.clear()  # Clear previous frame
    plot_map(ax, y_list[frame])  # Plot the map for the current frame
    ax.set_title(y_list[frame])  # Set title for the current frame

# Function to plot the map for a specific year
def plot_map(ax, year):
    #ax.set_xlim(-126.5, -102.4)
    #ax.set_ylim(30, 50)
    df_m.plot(column=year+'_v', ax=ax, edgecolor='white', vmin=40000, vmax=85000, alpha=0.9, cmap='viridis', legend=False)
    df_m.apply(lambda x: ax.annotate(text=x['name'] + '\n' + str(x[year]), color='white', xy=x.geometry.centroid.coords[0], ha='center'), axis=1)
    contextily.add_basemap(ax, source=contextily.providers.OpenStreetMap.Mapnik, zoom=5)

# Create a figure and axis for the animation
fig, ax = plt.subplots(figsize=(9, 6.5))

# Define the number of frames (equal to the number of years)
num_frames = len(y_list)

# Create the animation
ani = FuncAnimation(fig, update_map, frames=num_frames, interval=1000)

# Save the animation as a GIF
ani.save(r'C:\Users\Owner\Desktop\GA_visualization_proj\map_animation.gif', writer='Pillow')

plt.show()
#Open the downloaded GIF to see the wonders!

MovieWriter Pillow unavailable; using Pillow instead.


#extract color palette, the palette can be changed
import plotly.graph_objects as go
pal = list(sns.color_palette(palette='RdYlGn_r', n_colors=90000-40000).as_hex())
dict_pal = dict(zip(list(range(40000,90000)),pal))

for year, val in zip(y_list, v_list): 
    ax = df_m.plot(alpha=0.5, legend=False,  # Removed the 'column' parameter
                   edgecolor='black', color='white', figsize=(9, 6.5))

    n = 0.05  # adjust bubble size
    ax.scatter(df_m.geometry.centroid.x, df_m.geometry.centroid.y, s=df_m[val]*n,
               color=[dict_pal.get(i) for i in df_m[val]], alpha=0.9)

    df_m.apply(lambda x: ax.annotate(text=x['name'] + '\n' + str(x[year]),
                                     xy=x.geometry.centroid.coords[0],
                                     ha='center'), axis=1)
    
    ax = contextily.add_basemap(ax, source=contextily.providers.OpenStreetMap.Mapnik, zoom=5)

    plt.legend(title=year, frameon=False, bbox_to_anchor=(0.05, 0.05), loc='lower left')
    plt.tight_layout()
    plt.savefig(r'C:\Users\Owner\Desktop\GA_visualization_proj\bubble_'+year+'.png')

No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

	name	geometry	centroid	lon	lat
1	Montana	POLYGON ((-12378080.523 5552697.095, -12388918...	POINT (-12207797.775 5956345.924)	-1.220780e+07	5.956346e+06
4	Idaho	POLYGON ((-12361999.146 5541300.005, -12362056...	POINT (-12766050.772 5538968.009)	-1.276605e+07	5.538968e+06
5	Washington	POLYGON ((-13024165.874 5833417.950, -13013975...	POINT (-13402336.087 6007999.556)	-1.340234e+07	6.008000e+06
6	Arizona	POLYGON ((-12138858.896 4439094.580, -12138686...	POINT (-12430078.547 4074320.507)	-1.243008e+07	4.074321e+06
7	California	POLYGON ((-12729929.314 4091367.372, -12708897...	POINT (-13326005.299 4493440.166)	-1.332601e+07	4.493440e+06

	name	2021	2019	2018	2017	2016	2015	2014	2013	2012	2011	2010
0	United States	$69,717	$65,712	$63,179	$60,336	$57,617	$55,775	$53,657	$52,250	$51,371	$50,502	$50,046
1	Washington, D.C.	$90,088	$92,266	$85,203	$82,372	$75,506	$75,628	$71,648	$67,572	$66,583	$63,124	$60,903
2	Maryland	$90,203	$86,738	$83,242	$80,776	$78,945	$75,847	$73,971	$72,483	$71,122	$70,004	$68,854
3	Massachusetts	$89,645	$85,843	$79,835	$77,385	$75,297	$70,628	$69,160	$66,768	$65,339	$62,859	$62,072
4	New Jersey	$89,296	$85,751	$81,740	$80,088	$76,126	$72,222	$71,919	$70,165	$69,667	$67,458	$67,681

	name	geometry	centroid	lon	lat	2021	2019	2018	2017	2016	2015	2014	2013	2012	2011	2010
0	Montana	POLYGON ((-12378080.523 5552697.095, -12388918...	POINT (-12207797.775 5956345.924)	-1.220780e+07	5.956346e+06	$63,249	$57,153	$55,328	$53,386	$50,027	$49,509	$46,328	$46,972	$45,076	$44,222	$42,666
1	Idaho	POLYGON ((-12361999.146 5541300.005, -12362056...	POINT (-12766050.772 5538968.009)	-1.276605e+07	5.538968e+06	$66,474	$60,999	$55,583	$52,225	$51,807	$48,275	$47,861	$46,783	$45,489	$43,341	$43,490
2	Washington	POLYGON ((-13024165.874 5833417.950, -13013975...	POINT (-13402336.087 6007999.556)	-1.340234e+07	6.008000e+06	$84,247	$78,687	$74,073	$70,979	$67,106	$64,129	$61,366	$58,405	$57,573	$56,835	$55,631
3	Arizona	POLYGON ((-12138858.896 4439094.580, -12138686...	POINT (-12430078.547 4074320.507)	-1.243008e+07	4.074321e+06	$69,056	$62,055	$59,246	$56,581	$53,558	$51,492	$50,068	$48,510	$47,826	$46,709	$46,789
4	California	POLYGON ((-12729929.314 4091367.372, -12708897...	POINT (-13326005.299 4493440.166)	-1.332601e+07	4.493440e+06	$84,907	$80,440	$75,277	$71,805	$67,739	$64,500	$61,933	$60,190	$58,328	$57,287	$57,708

	name	geometry	centroid	lon	lat	2021	2019	2018	2017	2016	...	2011_v	2012_v	2013_v	2014_v	2015_v	2016_v	2017_v	2018_v	2019_v	2021_v
0	Montana	POLYGON ((-12378080.523 5552697.095, -12388918...	POINT (-12207797.775 5956345.924)	-1.220780e+07	5.956346e+06	$63,249	$57,153	$55,328	$53,386	$50,027	...	44222	45076	46972	46328	49509	50027	53386	55328	57153	63249
1	Idaho	POLYGON ((-12361999.146 5541300.005, -12362056...	POINT (-12766050.772 5538968.009)	-1.276605e+07	5.538968e+06	$66,474	$60,999	$55,583	$52,225	$51,807	...	43341	45489	46783	47861	48275	51807	52225	55583	60999	66474
2	Washington	POLYGON ((-13024165.874 5833417.950, -13013975...	POINT (-13402336.087 6007999.556)	-1.340234e+07	6.008000e+06	$84,247	$78,687	$74,073	$70,979	$67,106	...	56835	57573	58405	61366	64129	67106	70979	74073	78687	84247
3	Arizona	POLYGON ((-12138858.896 4439094.580, -12138686...	POINT (-12430078.547 4074320.507)	-1.243008e+07	4.074321e+06	$69,056	$62,055	$59,246	$56,581	$53,558	...	46709	47826	48510	50068	51492	53558	56581	59246	62055	69056
4	California	POLYGON ((-12729929.314 4091367.372, -12708897...	POINT (-13326005.299 4493440.166)	-1.332601e+07	4.493440e+06	$84,907	$80,440	$75,277	$71,805	$67,739	...	57287	58328	60190	61933	64500	67739	71805	75277	80440	84907

Import the necessary libraries¶

Using the contextily library to make the chart look realistic. Add Basemap¶

Using this with U.S. states and territories by income information on Wikipedia¶

Merge Operation¶

Convert Household Income from string to Integers¶

Visualization¶

Using spatial areas and colors with Choropleth maps¶

Creating a photo collage - Using Matplotlib.pyplot¶

Creating an animation¶

Comparing sizes with Bubble charts¶